Gage locations
USGS2 and DS2 have the same coordinates. USGS1 and DS3 have the same
coordinates.
Hydrographs
Notes on gages: So far DS2 and DS3 have not been used. US1 and DS1
are mostly point measurements.
Streamflow differencing between each gage
DS1 minus US1 (uppermost)
Average DS1 minus US1 difference
## [1] 0.03090149
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
0.0232853
|
|
2012
|
0.0138214
|
|
2013
|
0.0334710
|
|
2014
|
0.0497037
|
|
2015
|
0.0665000
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
USGS2 minus DS1
Average USGS2 minus DS1 difference
## [1] 0.04210856
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
0.0363444
|
|
2012
|
0.0106020
|
|
2013
|
0.0944612
|
|
2014
|
NaN
|
|
2015
|
NaN
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
USGS1 minus USGS2
Average USGS1 minus USGS2 difference
## [1] -0.0001941755
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
-0.0231274
|
|
2012
|
0.0108739
|
|
2013
|
0.0138716
|
|
2014
|
NaN
|
|
2015
|
NaN
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
Sum of losses between USGS1 and USGS2
|
Year
|
Month
|
Monthly sum of losses (m3/s)
|
|
2011
|
4
|
-0.3270991
|
|
2011
|
5
|
-1.0456047
|
|
2011
|
6
|
-3.5999679
|
|
2011
|
7
|
0.0700276
|
|
2011
|
8
|
0.6986662
|
|
2011
|
9
|
-0.0434309
|
|
2012
|
4
|
1.9742787
|
|
2012
|
5
|
0.4860510
|
|
2012
|
6
|
0.1782581
|
|
2012
|
8
|
0.2018424
|
|
2012
|
9
|
-0.0854056
|
|
2013
|
5
|
2.4741706
|
|
2013
|
6
|
-0.5456931
|
|
2013
|
7
|
-0.3966977
|
|
2013
|
8
|
-0.5607375
|
Compare Niwot SNOTEL pirecip to prism pixel
niwot_prism <- read.csv(here('data', 'niwot_prism_compare.csv')) %>%
filter(!row_number() %in% c(1:10)) %>%
rename(date = 1,
prism_mm = 2) %>%
mutate(date = ymd(date),
prism_mm = as.numeric(prism_mm))
niwot_snotel <- prec_update %>%
dplyr::select(c(date, Niwot_Snotel)) %>%
rename(snotel_mm = Niwot_Snotel) %>%
mutate(date = dmy(date))
niwot_compare <- full_join(niwot_prism, niwot_snotel, by = 'date')
niwot_compare <- niwot_compare %>%
mutate(diff_mm = snotel_mm - prism_mm) %>%
na.omit()
#filter to look at sept - june
niwot_winter_comp <- niwot_compare %>%
mutate(month = month(date)) %>%
filter(!month %in% c(6:8))
summary(niwot_winter_comp)
## date prism_mm snotel_mm diff_mm
## Min. :2011-01-01 Min. : 0.000 Min. : 0.000 Min. :-63.69000
## 1st Qu.:2012-10-24 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: -1.21000
## Median :2014-05-17 Median : 0.410 Median : 0.000 Median : 0.00000
## Mean :2014-06-27 Mean : 2.613 Mean : 2.577 Mean : -0.03675
## 3rd Qu.:2016-03-09 3rd Qu.: 2.910 3rd Qu.: 3.000 3rd Qu.: 0.94000
## Max. :2017-12-31 Max. :68.220 Max. :58.000 Max. : 41.32000
## month
## Min. : 1.000
## 1st Qu.: 3.000
## Median : 5.000
## Mean : 6.358
## 3rd Qu.:10.000
## Max. :12.000
niwot_prismshift <- niwot_compare %>%
mutate_at(c("prism_mm"), funs(lead), n = 1) %>%
mutate(month = month(date)) %>%
filter(!month %in% c(6:8)) %>%
mutate(diff_mm = snotel_mm - prism_mm)
summary(niwot_prismshift)
## date prism_mm snotel_mm diff_mm
## Min. :2011-01-01 Min. : 0.000 Min. : 0.000 Min. :-15.22000
## 1st Qu.:2012-10-24 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: -0.29000
## Median :2014-05-17 Median : 0.410 Median : 0.000 Median : 0.00000
## Mean :2014-06-27 Mean : 2.611 Mean : 2.577 Mean : -0.03343
## 3rd Qu.:2016-03-09 3rd Qu.: 2.910 3rd Qu.: 3.000 3rd Qu.: 0.00000
## Max. :2017-12-31 Max. :68.220 Max. :58.000 Max. : 9.27000
## NA's :1 NA's :1
## month
## Min. : 1.000
## 1st Qu.: 3.000
## Median : 5.000
## Mean : 6.358
## 3rd Qu.:10.000
## Max. :12.000
##